DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
Claims 1-8 are pending and examined herein per Applicant’s 07/30/2026 filing with the USPTO. Claims 1, 7, and 8 are amended. Claim 6 is canceled. No claims are withdrawn or newly added.
Second action non-final is being issued because upon further review of the claims it was determined that a 35 USC 101 rejections should have been issued in the first action.
Response to Arguments
Applicant’s arguments, filed with respect to drawings objections of the previous Office action have been fully considered and are persuasive. The drawings objections have been withdrawn.
Applicant’s arguments are directed towards newly added limitations that are fully addressed in the updated rejection below.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-8 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea (i.e. certain methods of organizing human activity and mental processes) without practical application or significantly more when the elements are considered individually and as an ordered combination.
Step 1: Is the claimed invention to a process, machine, manufacture or composition of matter?
Yes, the claims fall within at least one of the four categories of patent eligible subject. Claims 1-5 are to a system (machine). Claim 7 is to a method (process).
No, claim 8 does NOT fall within a statutory class, see additional rejection at 8.
Step 2A, prong 1: Does the claim recite an abstract idea, law or nature, or natural phenomenon?
Yes, the claims are found to recite an abstract idea. Specifically, the abstract idea of certain methods of organizing human activity and mental processes.
Where certain methods of organizing human activity include fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions) (see MPEP § 2106.04(a)(2), subsection II).
Where mental processes relates to concepts performed in the human mind (including an observation, evaluation, judgment, opinion) (see MPEP § 2106.04(a)(2), subsection III).
Claim 1 (as a representative claim) recites the following, where the limitations found to contain elements of the abstract idea are in bold italics:
1. (Currently Amended) A worker evaluation system comprising:
a calculation unit;
an input/output unit; and
a storage unit, wherein the storage unit stores:
worker actual working information in which a worker, a specific object, and specific object information that is information regarding a specific object related to a worker are associated;
overall performance information regarding overall performance of works of workers for the specific object; and
classification information that is information regarding a classification to which the specific object belongs,
the input/output unit accepts input of worker specification information that specifies a worker to be analyzed,
the calculation unit specifies
specific object information of the worker based on the worker specification information and the worker actual working information,
calculates individual performance information that indicates performance of a work of the worker regarding the specific object based on the specific object information,
compares the individual performance information and the overall performance information to calculate individual evaluation information that indicates evaluation of the worker for the specific object, and
specifies a similar specific object belonging to a classification similar to the classification to which the specific object belongs or a similar process of a specific object belonging to a classification similar to the classification to which the specific object belongs, based on classification information, and
the input/output unit outputs the individual evaluation information and the similar specific object or the similar process,
wherein
the calculation unit calculates the individual evaluation information for a plurality of specific objects,
the input/output unit accepts designation of a specific object to which the worker assigns high priority, and
the calculation unit extracts a specific object or a process in a classification similar to a classification to which a specific object designated as having relatively high priority belongs among specific objects to which relatively high individual evaluation is provided.
The claimed invention is directed to the evaluation of a worker for a specific object based on how the worked has performed when compared to the overall performance for a specific object and the worker’s priority. The claimed invention is found to fall within the abstract categories of both certain methods of organizing human activity and a mental process.
The claims are to determining which worker will best perform the specific object thus the claims are viewed as managing personal behavior or relationships.
Further the but for the nominal recitation of the computing elements the claims could be reasonably performed in the mind of a human.
Step 2A, prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, the claimed invention does not recite additional elements that integrate the abstract idea into a practical application. Where a practical application is described as integrating the abstract idea by applying it, relying on it, or using the abstract idea in a manner that imposes a meaningful limit on it such that the claim is more than a drafting effort designed to monopolize it, see October 2019: Subject Matter Eligibility at p. 11.
The identified judicial exception is not integrated into a practical application. In particular, the claims recites the additional limitations see non-bold-italicized elements above. The input/output elements are determined to be insignificant extra solution activity.
Where 2106.05(g) MPEP states, “term "extra-solution activity" can be understood as activities incidental to the primary process or product that are merely a nominal or tangential addition to the claim. Extra-solution activity includes both pre-solution and post-solution activity. An example of pre-solution activity is a step of gathering data for use in a claimed process, e.g., a step of obtaining information about credit card transactions, which is recited as part of a claimed process of analyzing and manipulating the gathered information by a series of steps in order to detect whether the transactions were fraudulent. An example of post-solution activity is an element that is not integrated into the claim as a whole, e.g., a printer that is used to output a report of fraudulent transactions, which is recited in a claim to a computer programmed to analyze and manipulate information about credit card transactions in order to detect whether the transactions were fraudulent.”
The Office finds that merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea; adding insignificant extra solution activity to the judicial exception; or only generally linking the use of the abstract idea to a particular technological environment or field is not sufficient to integrate the judicial exception into a practical application.
Step 2B: Does the claim recite additional elements that amount to significantly more than the abstract idea?
No, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception, when considered individually and as part of the ordered combination. The computing elements are found to be general/generic when read in the light of the specification, see Spec. 78.
Where 2106.05(d)(I)(2) of the MPEP states, “A factual determination is required to support a conclusion that an additional element (or combination of additional elements) is well-understood, routine, conventional activity. Berkheimer v. HP, Inc., 881 F.3d 1360, 1368, 125 USPQ2d 1649, 1654 (Fed. Cir. 2018). However, this does not mean that a prior art search is necessary to resolve this inquiry. Instead, examiners should rely on what the courts have recognized, or those in the art would recognize, as elements that are well-understood, routine, conventional activity in the relevant field when making the required determination. For example, in many instances, the specification of the application may indicate that additional elements are well-known or conventional. See, e.g., Intellectual Ventures v. Symantec, 838 F.3d at 1317; 120 USPQ2d at 1359 ("The written description is particularly useful in determining what is well-known or conventional"); Internet Patents Corp. v. Active Network, Inc., 790 F.3d 1343, 1348, 115 USPQ2d 1414, 1418 (Fed. Cir. 2015) (relying on specification’s description of additional elements as "well-known", "common" and "conventional"); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 614, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (Specification described additional elements as "either performing basic computer functions such as sending and receiving data, or performing functions ‘known’ in the art.").”
These limitations do NOT offer an improvement to another technology or technical field; improvements to the functioning of the computer itself; apply the judicial exception with, or by use of, a particular machine; effect a transformation or reduction of a particular article to a different state or thing; add a specific limitation other than what is well-understood, routine and conventional in the field, or add unconventional steps that confine the claim to a particular useful application; or other meaningful limitations beyond generally linking the use of the judicial exception to a particular technological environment. Therefore, these additional limitations when considered individually or in combination do not provide an inventive concept that can transform the abstract idea into patent eligible subject matter.
The other independent claims recite similar limitations and are rejected for the same reasoning given above.
The dependent claims do not further limit the claimed invention in such a way as to direct the claimed invention to statutory subject matter.
Claim 8 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter because the claim is to a computer storage medium. Where the specification provides support for the claimed element in “program source may be, for example, a storage medium (for example, a non-transitory storage medium) readable by a program distributing computer or a computer.”. (Instant Spec [20]). It is note that the “for example” does not limit the medium to a non-transitory one. The Subject Matter Eligibility of Computer Readable Media memorandum (01/26/2010) states “The broadest reasonable interpretation of a claim drawn to a computer readable medium . . . typically covers forms of non-transitory tangible media and transitory propagating signal per se . . . particularly when the specification is silent”, Also see MPEP 2106.03(I). It is suggested that Applicant add “non-transitory” to the claimed limitation to overcome the rejection.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-8 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Nath et al (US 2015/0317582 A1).
Claim 1
Nath teaches worker evaluation system comprising (Nath [24] “ the system diagram of FIG. 1 illustrates the interrelationships between program modules for implementing various embodiments of the Context-Aware Crowdsourced Task Optimizer”):
a calculation unit (Nath [150] “portable electronic devices, wearable computing devices, hand-held computing devices, laptop or mobile computers, communications devices such as cell phones, smartphones and PDA's, multiprocessor systems, microprocessor-based systems, set top boxes, programmable consumer electronics, network PCs, minicomputers, audio or video media players, handheld remote control devices, etc”);
an input/output unit (Nath [153] “conventional computer input devices 540 or combinations of such devices (e.g., touchscreens, touch-sensitive surfaces, pointing devices, keyboards, audio input devices, voice or speech-based input and control devices, video input devices, haptic input devices, devices for receiving wired or wireless data transmissions, etc.) . . . conventional computer input devices 540 or combinations of such devices (e.g., touchscreens, touch-sensitive surfaces, pointing devices, keyboards, audio input devices, voice or speech-based input and control devices, video input devices, haptic input devices, devices for receiving wired or wireless data transmissions, etc.)”); and
a storage unit, wherein the storage unit stores (Nath [157] “any desired combination of computer or machine readable media or storage devices”):
worker actual working information in which a worker, a specific object, and specific object information that is information regarding a specific object related to a worker are associated (Nath [88] “a wide range of additional considerations or parameters (e.g., age, gender, fitness level, education, skills, worker's computing devices, tools, equipment, travel capabilities, quality reviews of worker or task result from task publisher, etc.)”);
overall performance information regarding overall performance of works of workers for the specific object (Nath [15] “worker history, present or expected worker locations, travel paths, working hours, skill set, capabilities of worker's fixed and mobile computing devices, etc.” and [16] “ machine learning techniques consider each workers history and behavior with respect to task types, task completion rates, contexts such as locations, travel direction, schedule, capabilities or skills of each worker (e.g., professional photographer, foreign language skills, etc.), capabilities of the workers mobile computing devices or tools (e.g., high resolution still or video cameras, laser rangefinders, microphones, etc.)”); and
classification information that is information regarding a classification to which the specific object belongs (Nath [18] “groups of human and/or virtual workers can also be “bundled” by the Context-Aware Crowdsourced Task Optimizer such that multiple workers can cooperate to perform particular tasks or task bundles. In the case of multiple workers acting as a group”),
the input/output unit accepts input of worker specification information that specifies a worker to be analyzed (Nath [30] “user input to determine and update the current and future worker contexts” and [50] “receives inputs including historical, real-time and future context information of multiple workers, and task properties (such as location, payment, deadline, etc.) from one or more task publishers. Given these inputs, the Context-Aware Crowdsourced Task Optimizer outputs recommended assignments of bundles of tasks to active workers.”),
the calculation unit specifies specific object information of the worker based on the worker specification information and the worker actual working information, calculates individual performance information that indicates performance of a work of the worker regarding the specific object based on the specific object information, compares the individual performance information and the overall performance information to calculate individual evaluation information that indicates evaluation of the worker for the specific object (Nath [52] “evaluate the learned worker models to return a probabilistic likelihood that particular workers will perform the particular task or bundle (optionally within some period of time).” And [85] “relative metric that can be evaluated in a variety of ways that may depend upon a workers skills, computing devices, tools, travel capabilities, etc.”), and
specifies a similar specific object belonging to a classification similar to the classification to which the specific object belongs or a similar process of a specific object belonging to a classification similar to the classification to which the specific object belongs, based on classification information (Nath [106] “each worker, there is an exponential number of different suitable task sets for which g.sub.ω(.) can be evaluated.”), and
the input/output unit outputs the individual evaluation information and the similar specific object or the similar process (Nath [57] “outputs recommended assignments of bundles of tasks to active workers”),
wherein
the calculation unit calculates the individual evaluation information for a plurality of specific objects (Nath [71] “learned model for each worker is then used by the Context-Aware Crowdsourced Task Optimizer to evaluate the likelihood that a particular worker will successfully complete a future task” and [106] “for each worker, there is an exponential number of different suitable task sets for which g.sub.ω(.) can be evaluated”),
the input/output unit accepts designation of a specific object to which the worker assigns high priority, (Nath [57] “receives inputs including historical, real-time and future context information of multiple workers, and task properties (such as location, payment, deadline, etc.) from one or more task publishers” and [93] “workers real-time and historical context information, predictive worker models, and task properties (such as location, budget, deadline, etc.)”, where priority is interpreted in light of the instant specification [88]. In Nath, the parameters are set by the worker, where the priority might be high pay or shortest drive as assessed by the worker) and
the calculation unit extracts a specific object or a process in a classification similar to a classification to which a specific object designated as having relatively high priority belongs among specific objects to which relatively high individual evaluation is provided (Nath [50] “the automatic bundling provided by the Context-Aware Crowdsourced Task Optimizer has been observed to improve workers' efficiency in terms of earnings per unit efforts (e.g., miles traveled, time to travel to task location, etc.)”, the bundle is the equivalent of the claimed classification where the limitation is made in the alternative only one element of the claim needs to be present in the art).
Claim 2
Nath teaches all the limitations of the worker evaluation system according to claim 1, wherein the similar process is a process in the same classification as the classification to which the specific object belongs or a process in a classification incorporating the classification to which the specific object belongs (Nath [52] and [62]).
Claim 3
Nath teaches all the limitations of the worker evaluation system according to claim 1, wherein the individual evaluation information includes information representing a degree of recommendation that recommends to allocate or not to allocate the worker to the similar process (Nath [16] and [68]), and
the degree of recommendation is a degree in accordance with the calculated evaluation of the worker (Nath [16] and [68]).
Claim 4
Nath teaches all the limitations of the worker evaluation system according to claim 3, wherein
in a case where the determined evaluation of the worker is relatively high, the degree of recommendation is a degree of how recommendable it is to allocate the worker to the similar process (Nath [16] and [68]), and
in a case where the determined evaluation of the worker is relatively low, the degree of recommendation is a degree of how recommendable it is not to allocate the worker to the similar process (Nash [72-73]).
Claim 5
Nath teaches all the limitations of the worker evaluation system according to claim 1, wherein the worker is a worker evaluated in advance (Nath [18]), and
the individual evaluation information includes information representing evaluation performed in advance, the evaluation factor, and the evaluation determined for the evaluation factor (Nath [30]).
Claim 6
Nath teaches all the limitations of the worker evaluation system according to claim 1, wherein
the calculation unit calculates the individual evaluation information for a plurality of specific objects (Nath [30] and [80]),
the input/output unit accepts designation of a specific object to which the worker assigns high priority (Nath [75]), and
the calculation unit extracts a specific object or a process in a classification similar to a classification to which a specific object designated as having relatively high priority belongs among specific objects to which relatively high individual evaluation is provided (Nath [50]).
Claim 7
Nath teaches a worker evaluation method to be performed by a computer, the worker evaluation method comprising (Nath [34]):
accepting input of worker specification information that specifies a worker to be analyzed (Nath [30] and [50]);
specifying specific object information of the worker based on worker specification information and worker actual working information (Nash [18]);
the worker actual working information being information in which a worker, a specific object, and specific object information that is information regarding a specific object related to a worker are associated (Nash [88]),
calculating individual performance information that indicates performance of a work of the worker regarding the specific object based on the specific object information (Nash [52] and [85]);
comparing the individual performance information and overall performance information to calculate individual evaluation information that indicates evaluation of the worker for the specific object (Nash [52] and [85]);
the overall performance information being information regarding overall performance of works of workers for the specific object (Nash [52] and [85]),
specifying a similar specific object belonging to a classification similar to a classification to which the specific object belongs or a similar process of a specific object belonging to a classification similar to the classification to which the specific object belongs, based on classification information that is information regarding the classification to which the specific object belongs (Nash [106]); and
outputting the individual evaluation information and the similar specific object or the similar process (Nash [57]).
Claim 8
Nath teaches a recording medium recording a computer program that causes a computer to execute (Nath [154-155]):
accepting input of worker specification information that specifies a worker to be analyzed (Nath [88]);
specifying specific object information of the worker based on worker specification information and worker actual working information (Nath [88]);
the worker actual working information being information in which a worker, a specific object, and specific object information that is information regarding a specific object related to a worker are associated (Nath [88]),
calculating individual performance information that indicates performance of a work of the worker for the specific object based on the specific object information (Nath [52] and [85]);
comparing the individual performance information and overall performance information to calculate individual evaluation information that indicates evaluation of the worker for the specific object, the overall performance information being information regarding overall performance of works of workers for the specific object (Nath [52] and [85]),
specifying a similar specific object belonging to a classification similar to a classification to which the specific object belongs or a similar process of a specific object belonging to a classification similar to the classification to which the specific object belongs, based on classification information that is information regarding the classification to which the specific object belongs (Nath [106]); and
outputting the individual evaluation information and the similar specific object or the similar process (Nath [57]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
LaReau et al (US 10,467,565 B2) teaches the system 100, high priority orders or projects may be processed at priority, thereby ensuring completion of these high priority, critical tasks on time.
Brown (US 2020/0411170 A1) teaches priority attribute information can include attributes that identify or indicate a priority level of a task (e.g., critical/urgent, high priority, low priority, medium priority, etc.). For example, in some implementations, the attribute defining component 312 can determine the priority level of a task based on the priority level being specified in the task definitions/requirement data.
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/FOLASHADE ANDERSON/Primary Examiner, Art Unit 3623
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